The demand for value purchasing in an era of cost-containment and changing health care delivery systems has dramatically increased the use of self-reported health status and quality of life information in clinical and policy decision-making. Although numerous instruments for measuring these constructs are available, measurement level and its influence on scientific inference and score interpretation have generally been overlooked. Measurement models, such a item response theory (IRT), have been used successfully in fields such as education psychology, sociology and political science, and health status researchers are beginning to advocate their use in health status assessment. However, only limited empirical investigation of their applicability in health status measurement have been performed. This study will investigate the usefulness of IRT models in generating precise estimates of health by applying these models on independent samples from condition-specific (asthma, end-stage renal disease) and norming (general population) studies. This study will focus on the physical functioning scale of the widely used health status measure, the Medical Outcomes Study Short-Form 36 (SF-36). The major IRT assumption of unidimensionality will be tested through factor analyses and an examination of item parameter stability. The contribution of two parameter and polytomous IRT models will also be examined through goodness-of-fit tests an analyses of residuals. The influence of different measurement models (summative, IRT) on aggregate scores will be examined through comparisons of errors of measurement, test information, magnitude and variability of scores for group differences and change over time, and predictive validity. This study will generate empirical evidence of the usefulness of item response models estimating health status. In addition, study results may be used to guide researchers selecting the most useful model for scoring their measures and in making valid scientific inferences and interpretations from their data. Practically, greater measurement precision could translate into lower costs of conducting research, by reducing measurement error. Ultimately, better measurement properties will enable more precise theoretical questions to be tested and more accurate clinical and policy predictions to be made.